Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach

Kim, D 2018, 'Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach', in Proceedings of the 21st International Conference on Information Fusion (FUSION 2018), Cambridge, United Kingdom, 10-13 July 2018, pp. 1438-1444.


Document type: Conference Paper
Collection: Conference Papers

Title Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach
Author(s) Kim, D
Year 2018
Conference name FUSION 2018
Conference location Cambridge, United Kingdom
Conference dates 10-13 July 2018
Proceedings title Proceedings of the 21st International Conference on Information Fusion (FUSION 2018)
Publisher IEEE
Place of publication United States
Start page 1438
End page 1444
Total pages 7
Abstract This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists.
Subjects Signal Processing
DOI - identifier 10.23919/ICIF.2018.8455261
Copyright notice © 2018 ISIF
ISBN 9780996452779
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 6 Abstract Views  -  Detailed Statistics
Created: Thu, 31 Jan 2019, 11:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us